Forecasting future trajectories of agents in complex traffic scenes requires reliable and efficient predictions for all agents in the scene. However, existing methods for trajectory prediction are either inefficient or sacrifice accuracy. To address this challenge, we propose ADAPT, a novel approach for jointly predicting the trajectories of all agents in the scene with dynamic weight learning. Our approach outperforms state-of-the-art methods in both single-agent and multi-agent settings on the Argoverse and Interaction datasets, with a fraction of their computational overhead. We attribute the improvement in our performance: first, to the adaptive head augmenting the model capacity without increasing the model size; second, to our design choices in the endpoint-conditioned prediction, reinforced by gradient stopping. Our analyses show that ADAPT can focus on each agent with adaptive prediction, allowing for accurate predictions efficiently. https://KUIS-AI.github.io/adapt

翻译：在复杂交通场景中预测智能体的未来轨迹，需要对场景中所有智能体做出可靠且高效的预测。然而，现有轨迹预测方法要么效率低下，要么牺牲准确性。为解决这一挑战，我们提出 ADAPT——一种通过动态权重学习联合预测场景中所有智能体轨迹的新方法。在 Argoverse 和 Interaction 数据集上，我们的方法在单智能体和多智能体设置中均优于最先进方法，且计算开销仅为它们的零头。我们将性能提升归因于：第一，自适应头部在不增加模型尺寸的情况下增强了模型容量；第二，端点到端点条件预测的设计选择，并通过梯度停止加以强化。分析表明，ADAPT 能够通过自适应预测聚焦于每个智能体，从而高效实现精确预测。https://KUIS-AI.github.io/adapt